Envisioning Distant Worlds: Fine-Tuning a Latent Diffusion Model with NASA's Exoplanet Data

There are some 5,500 confirmed Exoplanets beyond our solar system. Though we know these planets exist, most of them are too far away for us to know what they look like. In this paper, we develop an algorithm and a model to translate any given exoplanet’s numeric data into a text prompt that can be input into a trained latent diffusion model to generate a predictive visualization of that exoplanet. This paper describes a novel approach of translating numeric data to textual descriptors formulated from prior accepted astrophysical research. These textual descriptions are paired with photographs and artistic visualisations from NASA’s public archives to build a training set for a latent diffusion model, which can produce new visualizations of unseen distant worlds. Workshop on Machine Learning for Creativity and Design NeurIPS 2023 Workshop

Stories from the Flood: Promoting Healing and Fostering Policy Change Through Storytelling, Community Literacy, and Community-based Learning

This profile features the authors\u27 shared work to co-create both a community literacy project, Stories from the Flood, and the undergraduate community-based learning courses that supported the effort. Stories from the Flood works to assist community members in southwestern Wisconsin to share their flood experiences, aiming to support community healing and serve as a resource for future conversations about flood recovery and resilience. Our collaboration on Stories from the Flood demonstrates the importance of non-university expertise and aims to daylight and correct structural asymmetries that render these rural watersheds both particularly vulnerable to flooding and absent of government intervention

Distinct Loci in the CHRNA5/CHRNA3/CHRNB4 Gene Cluster Are Associated With Onset of Regular Smoking

Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype

Increased Genetic Vulnerability to Smoking at CHRNA5 in Early-Onset Smokers

Recent studies have shown an association between cigarettes per day (CPD) and a nonsynonymous single-nucleotide polymorphism in CHRNA5, rs16969968.To determine whether the association between rs16969968 and smoking is modified by age at onset of regular smoking.Primary data.Available genetic studies containing measures of CPD and the genotype of rs16969968 or its proxy.Uniform statistical analysis scripts were run locally. Starting with 94,050 ever-smokers from 43 studies, we extracted the heavy smokers (CPD >20) and light smokers (CPD ≤10) with age-at-onset information, reducing the sample size to 33,348. Each study was stratified into early-onset smokers (age at onset ≤16 years) and late-onset smokers (age at onset >16 years), and a logistic regression of heavy vs light smoking with the rs16969968 genotype was computed for each stratum. Meta-analysis was performed within each age-at-onset stratum.Individuals with 1 risk allele at rs16969968 who were early-onset smokers were significantly more likely to be heavy smokers in adulthood (odds ratio [OR] = 1.45; 95% CI, 1.36-1.55; n = 13,843) than were carriers of the risk allele who were late-onset smokers (OR = 1.27; 95% CI, 1.21-1.33, n = 19,505) (P = .01).These results highlight an increased genetic vulnerability to smoking in early-onset smokers